Following the previous works [1]}, [2]}, [3]}, we consider an unsupervised setting for anomaly detection in dynamic graphs. Specifically, in the training phase, no labeled data for anomaly is given, but we assume that all edges in the training set are normal. The binary labels of abnormality are given in the testing phase to evaluate the performance of algorithms. Concretely, a label \(y_{e_i} = 1\)  indicates that edge \(e_i\)  is an anomalous edge, and \(y_{e_i} = 0\)  indicates that \(e_i\)  is normal. Note that the anomaly label is often imbalanced, which means the number of normal edges is much larger than anomalous edges.
